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Concentration Inequalities for Conditional Value at Risk

Philip Thomas · Erik Learned-Miller

Pacific Ballroom #159

Keywords: [ Robust Statistics and Machine Learning ] [ Others ]


In this paper we derive new concentration inequalities for the conditional value at risk (CVaR) of a random variable, and compare them to the previous state of the art (Brown, 2007). We show analytically that our lower bound is strictly tighter than Brown's, and empirically that this difference is significant. While our upper bound may be looser than Brown's in some cases, we show empirically that in most cases our bound is significantly tighter. After discussing when each upper bound is superior, we conclude with empirical results which suggest that both of our bounds will often be significantly tighter than Brown's.

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